In a high-volume online service environment, there is a need to identify, track, and process events that occur in the course of providing various services or features. As it is typical for millions of such events to be generated each day, these events are automatically generated, collected, and processed by computer systems. These events are typically processed periodically to generate reports useful for assessing the status of and managing the provided online services.
Of interest in the administration of online services are unexpected increases or reductions in traffic, whether for an online service as a whole, or for particular aspects of the online service, such as portions relating to customer service. Such changes in traffic volume may indicate a problem with the online service to which engineering or customer service-related resources need to be assigned for prompt resolution.
A conventional technique for assessing when such changes have occurred is the above-mentioned periodic generation of reports, in which events, whether stored in logfiles or otherwise, are processed to generate the reports. However, in view of the large number of events that are generated for a high-volume online service, the generation of such reports can require a significant amount of processing time. As a result, a significant amount of time may pass between an issue arising, and the detection or such an issue via this conventional technique for event processing.